While game-theoretic planning frameworks are effective at modeling multi-agent interactions, they require solving large optimization problems where the number of variables increases with the number of agents, resulting in long computation times that limit their use in large-scale, real-time systems. To address this issue, we propose 1) PSN Game-a learning-based, game-theoretic prediction and planning framework that reduces game size by learning a Player Selection Network (PSN); and 2) a Goal Inference Network (GIN) that makes it possible to use the PSN in incomplete-information games where other agents' intentions are unknown to the ego agent. A PSN outputs a player selection mask that distinguishes influential players from less relevant ones, enabling the ego player to solve a smaller, masked game involving only selected players. By reducing the number of players included in the game, PSN shrinks the corresponding optimization problems, leading to faster solve times. Experiments in both simulated scenarios and real-world pedestrian trajectory datasets show that PSN is competitive with, and often improves upon, the evaluated explicit game-theoretic selection baselines in 1) prediction accuracy and 2) planning safety. Across scenarios, PSN typically selects substantially fewer players than are present in the full game, thereby reducing game size and planning complexity. PSN also generalizes to settings in which agents' objectives are unknown, via the GIN, without test-time fine-tuning. By selecting only the most relevant players for decision-making, PSN Game provides a practical mechanism for reducing planning complexity that can be integrated into existing multi-agent planning frameworks.

翻译：尽管博弈论规划框架在多智能体交互建模方面效果显著，但其需要求解随智能体数量增加而变量数目增大的大型优化问题，导致计算耗时较长，限制了在规模化实时系统中的应用。为解决该问题，我们提出：1）PSN Game——一种基于学习的博弈论预测与规划框架，通过学习玩家选择网络（PSN）缩减博弈规模；2）目标推断网络（GIN），使PSN能在其他智能体意图对自车未知的不完全信息博弈中使用。PSN输出玩家选择掩码，区分关键影响玩家与次要玩家，使自车仅需求解包含已选玩家的缩减版博弈问题。通过减少纳入博弈的玩家数量，PSN缩小了相应优化问题的规模，从而加速求解。在仿真场景与真实行人轨迹数据集上的实验表明，PSN在预测准确性和规划安全性两方面均能与显式博弈论选择基线方法竞争，且常优于后者。在各场景中，PSN通常仅选择远少于完整博弈中实际存在的玩家数量，从而降低博弈规模与规划复杂度。此外，通过GIN模块，PSN可在无需测试时微调的条件下泛化至智能体目标未知的场景。通过仅选择与决策最相关的玩家，PSN Game为降低规划复杂度提供了可集成至现有多智能体规划框架的实用机制。